Improved Adaptive Genetic Algorithm Research Articles

Medical Supplies Delivery Route Optimization under Public Health Emergencies Incorporating Metro-based Logistics System

To effectively mitigate the spread of infections during public health crises, precise and timely distribution of medical supplies is crucial. This paper proposes the integration of the metro-based underground logistics system (M-ULS) into the delivery process to address the vehicle routing problem (VRP). Considering various factors related to the metro-based VRP in public health emergencies, we formulate a mixed-integer nonlinear function model that aims to minimize total delivery costs while maximizing the load factor of medical vans and the average demand index concerning demand urgency and satisfaction rate of medical centers. To achieve this, we develop an improved adaptive genetic algorithm (IAGA) that incorporates adaptive crossover, adaptive mutation, and an elitist strategy. A case study is conducted to verify and analyze the performance of the optimized model and solving algorithm. Extensive numerical experiments are carried out to assess the economical and efficient advantages of M-ULS and the computational efficiency and solution quality of IAGA. Sensitivity analysis is conducted to evaluate the impact of medical van capacity on the routes. Lastly, we consider inter-medical-center transfer (IMCT) to further enhance emergency health response.

Routing a mixed fleet of electric and conventional vehicles under regulations of carbon emissions

This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions.

A hybrid algorithm for inspection planning of subsea pipelines subject to corrosion-fatigue degradation

The corrosion-fatigue degradation is a significant concern causing subsea pipeline failure. This paper presents an inspection planning methodology for subsea pipelines subject to corrosion-fatigue degradation using dynamic Bayesian network (DBN) and improved Adaptive Genetic Algorithm (IAGA). The classical decision tree is used to represent the inspection process of pipeline. The corrosion-fatigue degradation is simulated using DBN to estimate the failure probability of subsea pipeline. IAGA is used to automatically present the inspection schemes and determine the optimal solution due to its strong optimization ability. A case study is utilized to illustrate the feasibility of the methodology. The results show that the cost is the lowest when the pipeline is inspected three times (11, 19 and 25 years) in its service, which is 58.0235 million dollars. The methodology can be used to guide the daily inspection planning of subsea pipeline subject to corrosion-fatigue degradation.

Mobile chargers scheduling algorithm for maximum data flow in wireless sensor networks

Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.

A Multi-Regional Collaborative Optimization Model of Emergency Medical Materials for Responding to COVID-19

Medical materials are the most important, fundamental resources necessary for emergency relief of major infectious disease disasters. The scientific and optimal allocation of emergency medical materials is the key to reducing casualties and losses in epidemic regions, and to improving the effectiveness and efficiency of rescue operations. In response to the cross-border characteristics of major infectious diseases, the imbalance of material storage, and the differences of supply across regions, a multi-objective optimization model for a multi-regional collaborative allocation of emergency medical materials was developed. Then, an improved adaptive genetic algorithm (IAGA) was designed and applied to solve the proposed model. Finally, a case study of the collaborative response to the COVID-19 epidemic in the Yangtze River Delta of China was conducted for model verification. The results show that collaborative allocation can improve the material satisfaction rate at demand points, especially under peak demand pressure during the early stage of the response, and can meet all material needs at all demand points in the shortest possible amount of time. The proposed model can achieve the effective integration and mutual sharing of emergency materials across regions, and improve the efficiency of emergency material utilization and rescue efforts. The material allocation scheme considers the difference coefficients in different regions, which is conducive to enhancing the flexibility of decision-making and the practical applicability of collaborative allocation operations. A comparative analysis of the algorithms shows that the proposed IAGA is an effective method for managing large-scale multi-regional emergency material allocation optimization problems, as it has higher solving efficiency, better convergence, and stronger stability.

Cuckoo search algorithm with fuzzy logic and Gauss–Cauchy for minimizing localization error of WSN

The location of the sensor node is critical in wireless sensor networks (WSN) as the information acquired by the sensor node may be worthless without knowing its source. However, high accurate positioning of sensor nodes remains a big challenge. To overcome the barrier, an improved cuckoo search algorithm with fuzzy logic and Gauss–Cauchy strategy (ICS-FG) is proposed, that integrates the meta-heuristic algorithm with the traditional method. To regulate the dynamic adjustment of parameters, our study proposes a fuzzy logic based on population diversity. The proposed Gauss–Cauchy strategy significantly improve the algorithm’s search accuracy while enhancing its robustness when evaluated on several selected benchmark functions and locating unknown nodes in WSN. Experimental results obtained from well-known benchmark functions demonstrate the advance of the ICS-FG approach over the parallel compact cuckoo search algorithm (pcCS), improved adaptive genetic algorithm (IAGA), and other remarkable methods. By quantitative assessment, the proposed ICS-FG approach achieves a lower positioning error than pcCS, IAGA, and other state-of-the-art algorithms.

Topology Design and Optimization of Train Communication Network Based on Industrial Ethernet

With increasing requirements for the amount of data transmitted by train communication networks (TCN), the industrial Ethernet has become a solution with large transmission rates and high compatibility. To ensure safety and real-time communication, the delay of data transmission must be strictly limited in TCN. However, the delay caused by data transmission conflicts is still a problem in the current Ethernet research, which cannot meet the demands of real-time transmission between network devices. To solve the time-delay problem, an improved adaptive genetic algorithm (IAGA) for topology optimization is proposed, in which a new optimization model is established based on the train Ethernet topology. Compared with two traditional algorithms in solving the optimization model, the IAGA achieved better performance in terms of convergence speed and solution accuracy. To verify the effect of the optimized model, the theoretical calculation, simulation, and experiment based on a communication platform are carried out for the real-time index. Experimental results indicated that the end-to-end delay of the optimized network is significantly reduced. In designing and solving the topology optimization problem of TCN, the proposed model and algorithm effectively realized real-time improvement of the data transmission.

Automatic Recognition of Geomagnetic Suitability Areas for Path Planning of Autonomous Underwater Vehicle

Currently, integrated navigation systems with the inertial navigation system (INS)/geomagnetic navigation system (GNS) have been widely used in underwater navigation of autonomous underwater vehicle (AUV). Restricting AUV to navigate in the geomagnetic suitability areas (GSA) as far as possible can effectively improve the accuracy of integrated navigation systems. In order to improve the classification accuracy of GSA, a new optimal classification method based on principal component analysis (PCA) and improved back propagation (BP) neural network is proposed. PCA is used to extract the independent characteristic parameters containing the main components. Then, considering similarity coefficient, the initial weights and thresholds of BP neural network is optimized by improved adaptive genetic algorithm (IAGA). Finally, the correspondence between the geomagnetic characteristic parameters and matching performance is established based on PCA and improved adaptive genetic algorithm and back propagation (IAGA-BP) neural network for the automatic recognition of GSA. Simulated experiments based on PCA and IAGA-BP neural network shows high classification accuracy and reliability in the GSA selection. The method could provide important support for AUV path planning, which is an effective guarantee for AUV high precision and long voyage autonomous navigation.

An improved adaptive genetic algorithm based on DV-Hop for locating nodes in wireless sensor networks

The localization problem of unknown nodes in wireless sensor networks (WSN) has drawn increasing scholarly attention along together the popularity of meta-heuristic algorithms. To overcome the shortcomings of low accuracy that traditional least square method (LSM) inevitably produces, this paper introduces an improved adaptive genetic algorithm (IAGA) to handle the aforementioned problem and uses a modified evaluation function to reduce the error of distance measurement in a topological structure. The experimental results prove that the IAGA algorithm based on DV-Hop has superior performance in comparison of original DV-Hop and other meta-heuristic algorithms. The conclusion can be drawn that meta-heuristic algorithms have an better superiority over DV-Hop in locating nodes in WSN and the IAGA is more promising than other meta-heuristic algorithms.

Thermal error modeling and compensation of multilink high-speed precision press system

It is essential to develop the predictive model of thermal error for multilink high-speed precision press system (MHSPPS) in order to construct its compensation on the slider’s position accuracy at the bottom dead point (BDP). In this work, based on the principal factor strategy and the clustering methodology, the locations of thermal sensitive points (TSPs) on the MHSPPS are determined and the corresponding temperature is taken as the feed in parameter of the thermal error model (TEM). An improved adaptive genetic algorithm (IAGA) incorporated with a back-propagation neural network (BPNN) is then presented so as to simulate and predict the thermal error of MHSPPS through the collected temperature of TSPs. Compared with the experimental results, accuracy of the predictive model forward goes beyond that of the traditional TEMs, such as multiple linear regression (MLR), genetic algorithm with BPNN (GA-BPNN), and particle swarm optimization with BPNN (PSO-BPNN) models, which proves effectiveness of the proposed model. In order to implement the compensation experiment, a novel adjustment mechanism of slider’s BDP position for MHSPPS is designed and the compensation algorithm with online correction based on the IAGA-BPNN model is also proposed. Test results indicate that the maximum errors and the RMS errors of the slider’s BDP position after compensation with online correction based on the proposed IAGA-BPNN model can be reduced to 93.33% and 98.75%, respectively, which verifies the authenticity of thermal error compensation based on the IAGA-BPNN model and online correction algorithm.

Research on the Prediction Method of Ultimate Bearing Capacity of PBL Based on IAGA-BPNN Algorithm

In order to better predict ultimate bearing capacity of perfobond leiste shear connection (PBL), the six specimens were designed for push-out test, and the prediction models were built based on an Improved Adaptive Genetic Algorithm (IAGA) and Back Propagation neural network (BPNN) algorithm. With the finite element model established, it was found that the effects of different parameters on the ultimate bearing capacity of PBL vary greatly if using a single parameter method. The calculation results showed that transverse reinforcement diameter, hole diameter of steel plate, the thickness of steel plate and the strength grade of concrete were the four key factors affecting the ultimate bearing capacity of PBL. In order to overcome the disadvantages of BPNN, such as slow convergence speed and easy to fall into local optimization, an improved adaptive genetic algorithm is used to optimize the initial weights and thresholds of BPNN. The comparison shows that the algorithm is superior to the standard genetic algorithm and other heuristic algorithms, in terms of convergence speed, global search ability and robustness. The IAGA-BPNN prediction model was established. Using the experimental data obtained from both the fatigue test and the references as samples to train the prediction model, the results show that the IAGA-BPNN algorithm proposed in this article can accurately predict the ultimate bearing capacity of PBL, with an average error of 1.69%. The comprehensive sensitivity analysis (CSA) method adopts to explore the relative contribution of each key factors and the interaction between the key factors, and the analysis results show that the ultimate bearing capacity of PBL increases significantly with the increase of the thickness of steel plate and hole diameter of steel plate. The accuracy and stability were better than formulas for calculating the ultimate bearing capacity and the BP neural network prediction algorithm.

Improved Genetic Algorithm to Optimize the Wi-Fi Indoor Positioning Based on Artificial Neural Network

In order to make more effective use of Wi-Fi fingerprint data to position an object, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the BP (Back Propagation) neural network, namely, IAGA-BP. In this method, the selection, crossover and mutation operations of the genetic algorithm are used to optimize the weights and biases of the BP neural network. On the one hand, the proposed algorithm improves the selection operator in the adaptive genetic algorithm on the basis of preserving the optimal strategy. That is, the population of each generation will be sorted according to the adaptability from the highest to the lowest, then the highest 20% of the population will be directly inherited to the next generation while the worst 20% will be eliminated. The remaining 80% of the population will be selected by a roulette algorithm based on the selection probability of each individual, as to ensure the population volume unchanging. On the other hand, the crossover and mutation probability formulas in the adaptive genetic algorithm are improved. The crossover and mutation rates will be adjusted to preserve superior individuals and genes according to the level of individual adaptability and the current evolution stage of the population. The simulation results show that compared with the traditional Wi-Fi positioning method, the proposed Wi-Fi positioning method has a faster convergence speed and better positioning accuracy of 2.48 meters.

An Improved Spatio-Temporal Kriging Interpolation Algorithm and Its Application in Slope

Slope stability analysis based on the deformation monitoring data has been commonly used to predict and warn slope disasters. However, due to breakdown of the monitoring equipment or restrictions and interferences of internal and external factors in the area, the loss of data is unavoidable during the process of the slope monitoring. This problem can be solved by the spatio-temporal Kriging interpolation algorithm. However, the subjective factors and theoretical semi-variogram of variogram models, and many parameters estimation may lead to low interpolation precision and poor calculation efficiency. In this paper, a hybrid spatio-temporal interpolation algorithm was presented by combining the improved adaptive genetic algorithm (IAGA) with the spatio-temporal Kriging interpolation method, and it was applied to monitor the deformation of HP1 slope in Yuebao open-pit mine. The results showed that the interpolation precision of the proposed method was about 1 times higher than that of the traditional spatio-temporal Kriging method and the spatio-temporal method of Gaussian process regression method. In addition, after the interpolation analysis of the missing part of the monitoring data, the maximum cumulative displacement of all monitoring points was around 35.8 mm, and the rate of the displacement deformation didn't exceed 2 mm/d for three consecutive days. The deformation trend was basically consistent with the actual slope. It shows that the improved spatio-temporal Kriging interpolation algorithm (ISTKIA) has certain feasibility and reliability, and could provide a new idea for the research of related problems in related fields.

A Novel Method for Hourly Electricity Demand Forecasting

Short-term load forecasting has been playing an increasingly important role in electric power systems. Effective forecasting of the future electricity demand, however, is difficult in view of the complicated effects on load by a variety of factors. A hybrid method based support vector regression (SVR) with meteorological factors and electricity price is proposed to address such problem. First, the input used in this paper, are specific ratio value combinations of each characteristic parameter affecting the hourly electricity load. Second, SVR is used to analyze and develop a load forecasting model. Third, an improved adaptive genetic algorithm (IAGA) is utilized to optimize the specific ratio value combinations of each characteristic parameter, penalty factor C and Gaussian kernel function σ to accurately establish a forecasting model. The experimental results show that the proposed method can obtain better forecasting performance in comparison with other standard and state-of-the-art methods.

An Improved Adaptive Genetic Algorithm for Optimizing Pyroshock Acceleration Synthesis

The shock response spectrum (SRS) applied to pyroshock designs and tests for space systems does not include input acceleration time history; therefore, it cannot be used directly in structural nonlinear dynamic analysis before acceleration time history is synthesized. Existing synthesis methods typically rely on certain experimental data. In this study, a combined method for pyroshock acceleration synthesis is presented using a series of wavelets at low frequencies and damped sines at medium-high frequencies in the absence of experimental data. To improve the quality of the synthesized SRS, we develop an improved adaptive genetic algorithm (IAGA) with nonlinear adaptive adjustments of crossover and mutation probabilities. Numerical tests show that combined with the developed IAGA, the new method achieves higher accuracy and practicability in the synthesis of pyroshock acceleration compared with traditional methods. This work is expected to improve the calculation accuracy of spacecraft structure response under pyroshock loads.

An improved variational mode decomposition method and its application in diesel engine fault diagnosis

The diesel engine is a complex mechanical device, with the characteristics of multi-source, multi moving parts, complex work. For the complex multi-component signal, it is usually necessary to decompose it into a number of single-component AM-FM signals, and each component is analyzed to extract amplitude and frequency information. VMD is essentially composed of a plurality of adaptive Wiener filter and has good noise robustness. Compared with EMD, EEMD, CEEMDAN, LMD and ITD, VMD has strong mathematical theory basis. At the same time, VMD rejects the method of recursive screening stripping. So VMD can effectively alleviate or avoid a series of problems which appear in other methods. However, it is a problem how to determine the number of decomposition layers and the penalty factor, because human factors will affect the decomposition results. In order to solve the problem, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the parameters of VMD. Genetic algorithms mainly include 3 genetic operators: selection, crossover and mutation. The cross probability and mutation probability will directly affect the optimization results. In the traditional genetic algorithm, the probability of cross and mutation are fixed, and the genetic algorithm is easy to fall into the local optimal. According to the regulation of hormone regulation, the cross probability and mutation probability in evolution were improved. The permutation entropy is a new method of mutation detection, which mainly aims at the spatial characteristics of the time series itself. Therefore, the entropy of the components obtained by the VMD decomposition is used as the fitness function of the IAGA. The modal number K and penalty factor α of VMD were iteratively optimized by IAGA, and the optimal combination of parameters was obtained. Based on the proposed method, the vibration signals of the crankshaft bearing fault simulation experiment were decomposed into several components. According to the value of the permutation entropy, the fault components were selected and the energy was extracted. The fault pattern is identified by the support vector machine (SVM) successfully. The simulation analysis and the simulation experiment of the crankshaft bearing fault show that the proposed method is effective. For the diagnosis of other engines, a large number of validation experiments are needed for further research.

A Member Selection Model of Collaboration New Product Development Teams Considering Knowledge and Collaboration

AbstractMember selection to form an effective collaboration new product development (Co-NPD) team is crucial for a successful NPD. Existing researches on member selection mostly focus on the individual attributes of candidates. However, under the background of collaboration, knowledge complementarity and collaboration performance among candidates are important but overlooked. In this paper, we propose a multi-objective optimization model for member selection of a Co-NPD team, considering comprehensively the individual knowledge competence, knowledge complementarity, and collaboration performance. Then, to solve the model, an improved adaptive genetic algorithm (IAGA) is developed. Finally, a real case is provided to illustrate the application of the model, and the IAGA is implemented to select the desired team members for optimal team composition. Additionally, the standard generic algorithm and particle swarm optimization are used to compare with the IAGA to further verify the effectiveness of the IAGA.

Path-oriented test cases generation based adaptive genetic algorithm.

The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software. The use of search-based optimization methods, such as genetic algorithms (GAs), has been proposed to handle this problem. This paper proposes an improved adaptive genetic algorithm (IAGA) for test cases generation by maintaining population diversity. It uses adaptive crossover rate and mutation rate in dynamic adjustment according to the differences between individual similarity and fitness values, which enhances the exploitation of searching global optimum. This novel approach is experimented and tested on a benchmark and six industrial programs. The experimental results confirm that the proposed method is efficient in generating test cases for path coverage.

An optimized rail crack detection algorithm based on population status

To improve efficiency and accuracy of wavelet packet decomposition method modified by simple genetic algorithm (SGA), a novel genetic algorithm, which is based on variance of population and population entropy, is proposed. And then wavelet packet decomposition method is optimized by this algorithm to detect rail cracks. In the optimized method, internal state of population and population diversity are linked up with evolutionary operations to adjust crossover-mutation operators of genetic algorithm. Further, a mathematical model describing fault signal is established, and its parameters are optimized to effectively extract information. The proposed algorithm was tested by test functions and simulated fault signals of rail cracks. The results about simulated fault signals show that convergence probability of proposed algorithm — at best — is 45% higher than that of SGA and 28% higher than that of improved adaptive genetic algorithm (IAGA), and accuracy of crack fault detection reaches above 92%. Meanwhile, the proposed algorithm isn’t prone to stagnation and has fast convergence speed and high accuracy of fault detection. This research not only improves performance of SGA, but also provides a new detection method for fault diagnosis of wheel-rail noise.

Minimization Number of Network-Coded Links Based on Improved Adaptive Genetic Algorithm for Multi-source Optical Networks

Abstract With the rapid development of multi-source optical multicast application, the wavelength division multiplexing (WDM) with limited number of wavelength channels is facing with the new challenge of bandwidth shortage. Optical multicast adopting network coding can improve the bandwidth utilization, but optical network coding needs to increase optical storage and computation overhead in WDM optical network. For reducing the number of optical network-coded links, an improved adaptive genetic algorithm (IAGA) is proposed to minimize the number of network-coded links for multicast. By designing the maximization difference crossover operation, IAGA can guarantee the diversity of population and avoid individuals from falling into a local optimal. By adaptively adjusting the crossover probability, IAGA makes the population diverse at the beginning stages and makes the excellent individuals remain in a stable condition. Compared with other algorithms, the simulation result shows that the proposed algorithm has fastest convergence speed, which means that it takes the shortest time to find the minimum numbers of coded link solutions.